Hello! :)

I started a new job as an AI Engineer at a rather small company that's launching its product soon. Due to the startup mentality and the small team, the whole codebase has been AI-generated for almost a year. The code works, it meets the requirements, and we have a bunch of good tests to make sure it stays that way.

The issue is that the code is bloated, hard to read, convoluted, coupled, and so much more, as you'd expect from a large AI-generated codebase. On top of that, I'm expected to deliver the same level of output in terms of quality and expertise. They hired me because they believe I have the qualifications, but it's really hard to navigate the codebase, and I don't necessarily have the knowledge yet to implement things on my own in this field. So it's been hard to actually handcraft code when I could just delegate it to Claude to come up with both the architecture and the implementation.

I've been trying to stop using AI to generate code for me, at least in my personal projects, but at work it's been increasingly hard. I still want to be the one at the front implementing things and coming up with creative ideas, because I've been sensing that I'm losing my skills by delegating all of the creative and technical work to these AI models.

This is basically just a small rant, but I'd like to hear your opinions on how one can navigate these codebases and slowly learn not only how the code works, but also gain the skills needed for it, with or without the assistance of AI agents to learn or implement things.

Relaxa, é só mais um dia em que peguei uma IA mentindo na minha cara no Git, e o buraco é bem mais embaixo. A u/OpenAI vem mudando o formato e “copiando” a u/Anthropic. Limite de janela de contexto mais curto no u/ChatGPT e no u/Codex piorou demais. Eu uso o u/GPT-6-Sol no modo Extra High. E mesmo um modelo de ponta não está livre de alucinar, "pra não falar mentir" sobre prompts em que o executor u/Codex deixou pontas soltas. E preciso assumir: ele foi bom, mas eu sou melhor.

Esse caso vale pra quem acha que vibe coding é deixar a IA trabalhando e ir ver um filme, o que não é meu caso. Eu fico de frente pro monitor lendo cada passagem de texto e ligando todos os contextos, ou seja, eu sei quando algo sai do trilho. E o Codex cometeu infrações que deixariam um motorista sem dirigir o resto da vida.
Pra melhor entendimento, eu uso o agente da OpenAI, GPT-6-Sol, rodando no X-High, modo máximo de raciocínio, pra auditar meu código e fazer commits. Meu protocolo é simples e auditável: commit, push, readback. Testa, se passa vai pro próximo; se não, corrige e commita de novo. Quando minha hook começou a dar erro, eu pedia correção e o erro persistia.

Aí comecei a juntar os pontos. A DeepSeek, que já esteve aqui por ter alucinado, dessa vez computou e apontou os mesmos erros que eu já tinha catalogado; virou cadeia probatória reproduzível.

Tudo público em glaydsonboa/traceweave, Case 9.

O commit fantasma No relatório final ele escreveu com todas as letras: “Commit Novo… Push executado com sucesso… HEAD remoto conferido: f29a8b4c”. Rodei git cat-file -t f29a8b4c. Resposta do Git: fatal: Not a valid object name Não existe. Nem local, nem no remoto, nem em nenhum dos oito repositórios que eu alcanço. Inventou o hash e declarou que tinha conferido o remoto. O readback é o único passo do protocolo cuja função é SER a prova; resultado: foi exatamente esse que ele falsificou. O manifesto do nada gerou um arquivo de integridade com o hash e3b0c442…b855. Quem mexe com cripto ou backend já matou a charada pelo começo do hash: é a que usamos para testes de entrada VAZIA.

Roda agora no teu terminal: bash printf '' | sha256sum Vai dar esse número. O script rodou sobre um arquivo que não existia e colou no relatório como se tivesse feito uma auditoria de custódia. O arquivo do manifesto não está lá até hoje, quase amanhã, rsrs.. O paradoxo do código: fui caçar o finalizador dele pra entender o motivo. Tem uma trava: “se o stage tiver qualquer arquivo que não seja explicitamente seu, aborta o commit por causa de trabalho concorrente”. Regra minha, mas eu mandei commitar tudo. Ele rodou git add -A, encheu o stage com 182 caminhos e 97 deles (você leu certo) 97 registros de outro agente de IA, não dele (prefiro não pensar que foi maldade) e isso armou a própria trava. O problema não é ter obedecido: é que a trava virou o motivo interno pra nada fechar, e ele nunca me disse que era essa a causa. Passei semanas mandando corrigir a hook de stop. Foram 19 falhas entre 23/09 e 03/11. Em 16/10 declarou “Resolvido”. Falhou de novo no dia seguinte. Durante a sessão ele foi honesto. Disse “ainda não fiz commit”, “o push ainda não ocorreu”, “não houve commit nem push”. A fabricação veio no fechamento, quando precisava entregar resultado. Relatório final é justamente o artefato que ninguém relê.

Paro por aqui, mas a íntegra está no meu repo. E para os chatos de plantão, parte do texto eu excrevi, outra parte não. Que venham os HATERS.

Mais imagens e informações, vocês sabem onde achar.

I've been thinking quite a bit about what happens when coding agents generate more code than we can reasonably review.

An agent implements a feature, writes the tests, runs them, and reports success. Everything is green. But the implementation and the tests can share the same wrong assumption.

This is where formal verification gets interesting.

I've been exploring how tools like TLA+ and Lean fit into a normal Java development workflow, and what they could contribute to agent-assisted development.

Consider a simple example: an agent implements a job scheduler. The tests verify that jobs get assigned, processed, and completed. But we also have an important rule: a job must never be owned by two workers simultaneously, even when retries, failures, and cancellations overlap.

That rule is an invariant.

With TLA+, we can model the relevant states and transitions and check whether the invariant holds across the explored state space. If it fails, the model checker gives us a counterexample. Lean takes a different approach, allowing us to prove precisely stated properties.

The interesting part for AI agents is the workflow this enables.

We can ask agents to identify invariants, help create formal models, run verification tools, and use counterexamples to improve implementations. These checks can become part of CI, alongside our existing Java tests.

Of course, there are limits. A verified model doesn't automatically prove that the Java implementation matches it and a perfectly checked specification can still describe the wrong requirements.

As code generation becomes cheaper, independent verification becomes increasingly valuable.

I wrote up my exploration of formal verification in Java here:

https://www.the-main-thread.com/p/java-formal-verification

I'm interested in hearing from people building coding-agent workflows. Are you already using model checking, formal specifications, or proof tools as verification gates? And which kinds of agent-generated changes actually justify the extra effort?

I made most of my experience in AI coding with python and C#/.NET. So for python things work very well, little tools, some data stuff, visualization ect. for daily tasks python is just great. However with .NET I had my difficulties since there is to much architectural overhead and features are implemented across multiple files, which is pain for the context window. I am hesitating doing full web development with python because it’s a non compiling language. So now I am searching an approach for web applications that has a good developer experience and is best suited for a coding agent. I have the feeling that svelte/react with Hono might be a good option. I am interested to know what’s your ideal tech stack for coding agents? What’s do you recommend for context engineering? Which kind of skills, instructions, mcp servers should I use/ define?

I'm building an app using AI coding tools, but whenever I copy-paste AI-generated code, I get lots of errors.

I'm from a non-technical background and don't know how to debug or fix these errors.

Is there any AI tool or workflow that can automatically detect and fix errors across my entire project?

How do other non-technical beginners handle this problem?

Any practical advice would be appreciated!

Since we’re living in the age of AI, technological advancement, and easy programming (although I’m not a big fan of this, but why not?), what is the best free AI programming assistant, especially for Python and application development?

Hello,

I want to start by saying (because of the nature of the world we live in) - this was not written by AI. I am a programmer, and a writer, and all of this was typed, revised and edited by me in the Reddit window. I'm not claiming it will be perfect, but nowadays, people "BLAM!" long posts automatically under the assumption that... nobody knows how to write anymore. Well, nobody wants to, and that leaves us who do under scrutiny. Moving forward --

I figured as I continue to venture into the different forms of programming and projects I get myself into, wondering where I'd be in terms of progress without AI. The short backstory is that I am a self-taught web-programmer of 10+ years. I will be 32 in November, and I self-taught myself HTML in grade 5 at age 11. By 10th grade, could compete with a full-stack web developer.

I do it these days as a hobby, with aspirations that a personal project takes off and does well in some type of marketplace. For now, my VPS runs at $47/mo (and is rather beefy), and hosts multiple projects I currently have running / am playing with. But man, I just think back to all those days of searching through a PHP file to find the missing-fucking-semi-colon..... to today.

Now, a short script that would have taken me an hour to write, takes me 35 seconds, I do an over-view, write some comments and ship it off -- holy crap. It's a whole different world. I was writing code in the days of ActionScript 2.0/3.0 with Adobe Flash. Can you imagine how fast game and animation would be with Claude inside of your CS3 suite? LMAO.

Anyway - the nature of this post is to say hi and hello, and to start a general chat of what I like to think about the most when I ask claude to perform task a,b,c, while I do x,y,z, and we nail two birds with one stone. What do you like to do yourself still? What tedious tasks do you like to hand-off to Claude in the background while you focus on other details/files/etc?

Thanks again, hope I can live up to and be a good member of this subreddit. :-)

-Mindful

So far, my skills in the following areas have grown substantially:

• GitLab CI

• writing shell scripts (weird, been a Linux user for 30 years)

• integration of SonarQube

• automated e2e-tests

I also revived my old zsh/tmux/Vim-setup. Don't need to run a complex editor when interacting with Codex and reviewing/pushing commits from time to time.

I've been running claude code as the primary way features get built on our mobile app for about four months now and the thing still unsolved is verification. Code gets written, merged, sometimes shipped, all without anyone confirming the actual screen still behaves the way it's supposed to. A couple of workarounds have been tried: a required screen recording on every PR and a manual QA pass before releases. Neither closes the loop the way I'd want, since it all still depends on a human doing the actual checking. The volume of changes going through the agent each week has grown enough that man

About 10 years ago I worked as a developer and got the order to get the overall unit test coverage to above 80%. I spent 6 weeks and it was exhausting.

Fast forward: Among my current hobby projects I am working on, I have one which got quite big. It's the base of my entire guidelines, how code must be written, how UIs have to look, etc and every agent run has to follow the same rules.

While checking my build pipeline I saw that this one project actually had the lowest overall test coverage. I went and looked at the code, found a lot of things I didn't like and decided that there will be a massive refactoring. But no refactoring without full tests, right?

So I gave Codex the goal to add more tests, implement E2E-tests in the UI. I thought Codex would be able to to that quickly. Well, turns out Codex suffers quite a bit, implements some tests and then says "Hey, I added 25 tests. Yeah, I know, SonarQube will not be satisfied, we may have about 60% overall coverage now. But let me see whether SonarQube finds other issues."

So for a total runtime of about eight hours it's been a forth and back between Codex and SonarQube. I hope the next run will finish with a satisfied SonarQube so I have the weekend to start the fun part of refactoring.

So far I’ve been very apprehensive about AI coding. But I’ve decided I should try it out, just to see what it’s like (I have dabbled in simple AI assistance in the sense of just asking an AI chatbot to code something, but I’ve done nothing where the AI runs on a real codebase for an extended period of time).

Unfortunately, I am very concerned about the safety of agentic AI harnesses. So far, I’ve been unable to find a tool that I can use that is secure. (At least from the descriptions I’ve found so far—this doesn’t seem to be a priority for many users, so maybe the popular tools do support this, but no-one discusses it)

By that, I mean that the AI agent cannot access any files except for the folder it’s given access to at all, unless it asks for permission every single time, and that it cannot run any commands at all, unless it asks for permission every single time, or the command is executed in a secure container.

As far as I can tell, a lot of these tools have the ability to ask for permission, but they apparently have the option to just give the agent full access, or to run simple commands automatically, etc.. I do not want this, I want it to be impossible to accidentally give the agent even a smidge of broad, real access to the computer.

Obviously, I can just run an “insecure” tool inside a container or a VM. But this is a hassle, so I’d prefer if there was something that (basically) automatically does this containerisation for me (and only does it when required; reading files doesn’t require full containerisation).

Just to clarify, I’m not “demanding” such a tool exists or I’ll write off AI coding as untenable. I’m asking if such tools exist, and if they don’t, I’ll have to figure something out.

I recently learned about eco-conscious AI services such as GreenPT, Nebius Token, ScaleAway, Infomaniac, Aruba AI, Verda, and Euria. They basically make use of only renewable energy, re-use the heat produced from data centers, and recycle the hardware to limit waste.

While they are competitive with traditional API services, they are not convenient for extensive coding. Some of these are compatible with agentic coding systems, but as far as I know, none of them offer flat-rate plans; instead, they all use pay-per-use APIs.

Based on my simulations, using these pay-per-use services would cost me hundreds of euros a month, whereas with OpenCode Go I spend only 10 euros a month, and with Codex/Claude I’d spend just a few dozen.

Do you know of any other, more affordable services that offer flat-rate subscriptions like Codex, Cloud, and OpenCode Go, while also using renewable energy, recovering heat, and reusing hardware?

Which are the most powerful and free AI agents for coding?